**Description:**
`langchain_huggingface` has a very large installation size of around 600
MB (on a Mac with Python 3.11). This is due to its dependency on
`sentence-transformers`, which in turn depends on `torch`, which is 320
MB all by itself. Similarly, the depedency on `transformers` adds
another set of heavy dependencies. With those dependencies removed, the
installation of `langchain_huggingface` only takes up ~26 MB. This is
only 5 % of the full installation!
These libraries are not necessary to use `langchain_huggingface`'s API
wrapper classes, only for local inferences/embeddings. All import
statements for those two libraries already have import guards in place
(try/catch with a helpful "please install x" message).
This PR therefore moves those two libraries to an optional dependency
group `full`. So a `pip install langchain_huggingface` will only install
the lightweight version, and a `pip install
"langchain_huggingface[full]"` will install all dependencies.
I know this may break existing code, because `sentence-transformers` and
`transformers` are now no longer installed by default. Given that users
will see helpful error messages when that happens, and the major impact
of this small change, I hope that you will still consider this PR.
**Dependencies:** No new dependencies, but new optional grouping.
- **Description:**
- In _infer_arg_descriptions, the annotations dictionary contains string
representations of types instead of actual typing objects. This causes
_is_annotated_type to fail, preventing the correct description from
being generated.
- This is a simple fix using the get_type_hints method, which resolves
the annotations properly and is supported across all Python versions.
- **Issue:** #31051
---------
Co-authored-by: Eugene Yurtsev <eyurtsev@gmail.com>
https://github.com/langchain-ai/langchain/pull/31286 included an update
to the return type for `BaseChatModel.(a)stream`, from
`Iterator[BaseMessageChunk]` to `Iterator[BaseMessage]`.
This change is correct, because when streaming is disabled, the stream
methods return an iterator of `BaseMessage`, and the inheritance is such
that an `BaseMessage` is not a `BaseMessageChunk` (but the reverse is
true).
However, LangChain includes a pattern throughout its docs of [summing
BaseMessageChunks](https://python.langchain.com/docs/how_to/streaming/#llms-and-chat-models)
to accumulate a chat model stream. This pattern is implemented in tests
for most integration packages and appears in application code. So
https://github.com/langchain-ai/langchain/pull/31286 introduces mypy
errors throughout the ecosystem (or maybe more accurately, it reveals
that this pattern does not account for use of the `.stream` method when
streaming is disabled).
Here we revert just the change to the stream return type to unblock
things. A fix for this should address docs + integration packages (or if
we elect to just force people to update code, be explicit about that).
Does not support partial images during generation at the moment. Before
doing that I'd like to figure out how to specify the aggregation logic
without requiring changes in core.
---------
Co-authored-by: Chester Curme <chester.curme@gmail.com>
Llama-3.1 started failing consistently with
> groq.BadRequestError: Error code: 400 - ***'error': ***'message':
"Failed to call a function. Please adjust your prompt. See
'failed_generation' for more details.", 'type': 'invalid_request_error',
'code': 'tool_use_failed', 'failed_generation':
'<function=brave_search>***"query": "Hello!"***</function>'***
Release core 0.3.63
Small update just to expand the list of well known tools. This is
necessary while the logic lives in langchain-core.
---------
Co-authored-by: Chester Curme <chester.curme@gmail.com>
Add image generation tool to the list of well known tools. This is needed for changes in the ChatOpenAI client.
TODO: Some of this logic needs to be moved from core directly into the client as changes in core should not be required to add a new tool to the openai chat client.
Added support for new Exa API features. Updated Exa docs and python
package (langchain-exa).
Description
Added support for new Exa API features in the langchain-exa package:
- Added max_characters option for text content
- Added support for summary and custom summary prompts
- Added livecrawl option with "always", "fallback", "never" settings
- Added "auto" option for search type
- Updated documentation and tests
Dependencies
- No new dependencies required. Using existing features from exa-py.
twitter: @theishangoswami
---------
Co-authored-by: Chester Curme <chester.curme@gmail.com>
**Description:** ConversationChain has been deprecated, and the
documentation says to use RunnableWithMessageHistory in its place, but
the link at the top of the page to RunnableWithMessageHistory is broken
(it's rendering as "html()"). See here at the top of the page:
https://python.langchain.com/api_reference/langchain/chains/langchain.chains.conversation.base.ConversationChain.html.
This PR fixes the link.
**Issue**: N/A
**Dependencies**: N/A
**Twitter handle:**: If you're on Bluesky, I'm @vikramsaraph.com
